gdrl
machine-learning-book
| gdrl | machine-learning-book | |
|---|---|---|
| 1 | 2 | |
| 747 | 2,863 | |
| - | - | |
| 10.0 | 6.8 | |
| about 2 years ago | 6 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gdrl
-
How to proceed further? (Learning RL)
I would recommend looking at Grokking Deep RL if you are looking for some hands on DRL practice in python without starting completely from scratch. You can find some of the jupyter notebooks here.
machine-learning-book
-
Implementing a ChatGPT-like LLM from scratch, step by step
Sorry, in that case I would rather recommend a dedicated RL book. The RL part in LLMs will be very specific to LLMs, and I will only cover what's absolutely relevant in terms of background info. I do have a longish intro chapter on RL in my other general ML/DL book (https://github.com/rasbt/machine-learning-book/tree/main/ch1...) but like others said, I would recommend a dedicated RL book in your case.
-
"Machine Learning with PyTorch and Scikit-Learn" book
All the code examples are available here: https://github.com/rasbt/machine-learning-book
What are some alternatives?
deep-rl-class - This repo contains the syllabus of the Hugging Face Deep Reinforcement Learning Course.
skorch - A scikit-learn compatible neural network library that wraps PyTorch
World4AI - World4AI is an educational resource for AI.
python-machine-learning-book-3rd-edition - The "Python Machine Learning (3rd edition)" book code repository
Andrew-NG-Notes - This is Andrew NG Coursera Handwritten Notes.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
baselines - OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
embedding-encoder - Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
Practical_RL - A course in reinforcement learning in the wild
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
Reinforcement-Learning - Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠